Cited by 1,126Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and AnimalsGenetic and phenotypic traits in livestock

A General Test of Association for Quantitative Traits in Nuclear Families

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Abecasis, G.R., Cardon, L.R., Cookson, W.O.C.·2000·The American Journal of Human Genetics

Abstract

High-resolution mapping is an important step in the identification of complex disease genes. In outbred populations, linkage disequilibrium is expected to operate over short distances and could provide a powerful fine-mapping tool. Here we build on recently developed methods for linkage-disequilibrium mapping of quantitative traits to construct a general approach that can accommodate nuclear families of any size, with or without parental information. Variance components are used to construct a test that utilizes information from all available offspring but that is not biased in the presence of linkage or familiality. A permutation test is described for situations in which maximum-likelihood estimates of the variance components are biased. Simulation studies are used to investigate power and error rates of this approach and to highlight situations in which violations of multivariate normality assumptions warrant the permutation test. The relationship between power and the level of linkage disequilibrium for this test suggests that the method is well suited to the analysis of dense maps. The relationship between power and family structure is investigated, and these results are applicable to study design in complex disease, especially for late-onset conditions for which parents are usually not available. When parental genotypes are available, power does not depend greatly on the number of offspring in each family. Power decreases when parental genotypes are not available, but the loss in power is negligible when four or more offspring per family are genotyped. Finally, it is shown that, when siblings are available, the total number of genotypes required in order to achieve comparable power is smaller if parents are not genotyped.

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  • Within-family genetic association estimates, such as those obtained from samples of siblings, can provide less biased estimates of direct genetic effects because they are unlikely to be affected by demographic and indirect genetic effects of parents.

    “Within-family genetic association estimates, such as those obtained from samples of siblings, can provide less biased estimates of direct genetic effects because they are unlikely to be affected by demographic and indirect genetic effects of parents7,17,30–34.”

    From Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects (2022) · cites this paper as [31]

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Authors
Abecasis, G. R., Cardon, L. R., & Cookson, W. O. C.
Affiliations
Centre for Human Genetics (GB)
Corresponding author
Abecasis, G.R.
Venue
The American Journal of Human Genetics
Publisher
Elsevier BV
Published
2000-01
Volume
66
Issue
1
Pages
279-292
ISSN
0002-9297 (ISSN-L)
OpenAlex
W2078625752
Cited by
1,126
References
30
Citations by year
2026: 2 · 2025: 3 · 2024: 7 · 2023: 7 · 2022: 6 · 2021: 13 · 2020: 15 · 2019: 13 · 2018: 18 · 2017: 23 · 2016: 22 · 2015: 26 · 2014: 27 · 2013: 38 · 2012: 51
FWCI
27.12
Type
journal article
Language
en
Open access
Locations
  • The American Journal of Human Genetics (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
Wellcome Trust
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
Keywords
association mapping, quantitative traits, nuclear family, variance components, permutation tests, fine mapping, power analysis, family structure, parental genotypes, multivariate normality, research design
MeSH terms
Genetics, Population, Humans, Models, Genetic, Nuclear Family, Linkage Disequilibrium, Quantitative Trait, Heritable

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